Method for calculating insulation resistance of transformer bushing under electromagnetic interference based on Wav-KAN algorithm

Abstract

The invention discloses a transformer bushing insulation resistance calculation method under electromagnetic interference based on a Wav-KAN algorithm, and relates to the technical field of transformer bushing insulation resistance calculation. The method comprises the following steps: S1, carrying out normalization processing on a measured value of the leakage current of the transformer bushing; s2, selecting a proper wavelet basis function and a proper number of decomposition layers, and extracting detail coefficients of each layer in wavelet coefficients to construct a detail coefficient matrix as a filtering input vector; s3, establishing a KAN filtering model, and obtaining the leakage current value of the transformer bushing after the induced current is filtered; and S4, constructing an empirical calculation model of the transformer bushing capacitance. According to the method for calculating the insulation resistance of the transformer bushing under the electromagnetic interference based on the Wav-KAN algorithm, when an empirical model of a transformer bushing capacitor is constructed, influences of environment temperature, humidity and the aging degree of the transformer bushing are considered, and an induced current part in a leakage current measurement value of the transformer bushing is filtered out; according to the method, the insulation resistance of the transformer bushing can be calculated more accurately.

Description

Technical Field

[0001] The invention relates to the technical field of transformer bushing insulation resistance calculation, and in particular to a transformer bushing insulation resistance calculation method under electromagnetic interference based on a Wav-KAN algorithm. Background Art

[0002] With the continuous development of the power system, it is of great significance to realize real-time monitoring of transformer insulation performance and ensure the normal operation of transformers for the normal production and life of society. However, due to the structure of the transformer itself, it is impossible to monitor the insulation resistance of the transformer itself in real time. The insulation performance of the transformer bushing can be obtained by measuring the leakage current of the transformer bushing and calculating the insulation resistance of the transformer bushing, so as to better reflect the insulation performance of the transformer. At higher voltage levels, stronger electromagnetic interference will be generated, resulting in induced current in the measurement process of the transformer bushing leakage current, making the calculation of the transformer bushing insulation resistance inaccurate. In view of this problem, if the induced current in the transformer bushing leakage current measurement value can be filtered out, it is of great significance to accurately calculate the insulation resistance of the transformer bushing and reflect the insulation performance of the transformer.

[0003] Wavelet Transform can decompose the signal into sub-bands of different frequencies, which is very useful for analyzing transformer bushing leakage current signals containing multiple frequency components and realizing signal denoising. Kolmogorov-Arnold Network (KANs) is a new network architecture that has been very popular recently. Compared with traditional multi-layer perceptrons (MLPs), learnable single-variable functions are used instead of nodes to replace linear weights, which can achieve higher accuracy and interpretability. At the same time, KANs have powerful function approximation capabilities. The detail coefficients of each layer can be constructed into a matrix form as the input of KAN, so that the network can learn the mapping relationship from these coefficients to the detail coefficients of each layer after filtering out the induced current. The Wav-KAN algorithm that combines the two can effectively capture complex data patterns, achieve faster training speed, and obtain higher accuracy. It has a good performance in removing the induced current in the transformer bushing leakage current measurement value.

[0004] Existing technologies such as CN109085461A use wavelet transform energy to calculate the single-phase high-resistance grounding impedance of the distribution network. By analyzing the current and voltage time domain waveforms, it can be obtained that when a high-resistance fault occurs, there is a mutation in the wavelet coefficient. By calculating the wavelet coefficient energy, it is found that the energy decreases to a certain extent before and after the high-resistance fault occurs, which can be used as a characteristic quantity for judging the occurrence of the fault. The processing idea is to use wavelet transform to calculate the wavelet coefficient energy, and then use it to train the regression model to predict the fault impedance value. It cannot be applied to calculate the insulation resistance value of the transformer bushing under electromagnetic interference.

[0005] Existing technologies such as CN110275114A, an online monitoring method for battery internal resistance based on a combined filtering algorithm, use DB10 wavelet basis function to perform 4-layer wavelet decomposition on the morphological filter signal, and perform soft-hard compromise threshold filtering on the detail signal, and then reconstruct the processed detail signal and approximate signal to obtain the response voltage signal and comparison voltage signal of the battery, and finally use the comparison method to calculate the equivalent internal resistance of the battery. It cannot be applied to calculate the insulation resistance value of the transformer bushing under electromagnetic interference.

[0006] Existing technologies such as CN114636900A, a distribution network multiple fault diagnosis method and system, use wavelet transform to denoise and collect the output training data of the neural network to form a relevant fault information decision table as a training sample for the neural network; in essence, it is still a method of forming a fault information decision table using a wavelet algorithm.

[0007] Existing technologies such as CN116125327A, a switching power supply fault diagnosis method based on improved wavelet denoising and LSTM, first designs the fault mode of the switching power supply, obtains the output voltage ripple of the switching power supply under different fault modes; selects appropriate wavelet basis functions and decomposition series to perform wavelet decomposition on the output voltage ripple; then, based on the coefficients after wavelet decomposition, estimates the threshold value through Bayesian; then uses the constructed threshold function to process the wavelet coefficients, and reconstructs the denoised output voltage signal through inverse wavelet transform; finally, uses the constructed LSTM model to perform fault diagnosis on the switching power supply based on the denoised and reconstructed signal. The purpose of its wavelet algorithm is denoising processing for switching power supply fault diagnosis. Summary of the invention

[0008] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a method for calculating the insulation resistance of transformer bushings under electromagnetic interference based on the Wav-KAN algorithm, which solves the technical problem of errors in the calculation of the insulation resistance of transformer bushings caused by electromagnetic interference generated at high voltage levels.

[0009] (II) Technical solution To achieve the above objectives, the present invention is implemented by the following technical scheme: a method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on Wav-KAN algorithm, comprising the following steps: S1: Collect the voltage value at both ends of the transformer bushing and the transformer bushing leakage current value, and normalize the measured value of the transformer bushing leakage current; S2: Select appropriate wavelet basis functions and decomposition layers, decompose the normalized transformer bushing leakage current value as the original signal, extract the detail coefficients of each layer in the wavelet coefficients to construct a detail coefficient matrix as the filter input vector; S3: Establish a KAN filter model, use the trained KAN filter model to process the input detail coefficient matrix, output the detail coefficient matrix after filtering out the induced current, reconstruct the signal to obtain the transformer bushing leakage current after filtering out the induced current; S4: Calculate the capacitance of the transformer bushing, the dielectric loss factor of the transformer bushing, and finally calculate the insulation resistance of the transformer bushing.

[0010] Preferably, the specific processing process of S1 is as follows: the voltage U1 at both ends of the transformer bushing and the transformer bushing leakage current I1 are measured by a voltage transformer and a current transformer. Through MAX-MIN normalization, the measured value I1 of the transformer bushing leakage current is normalized to between -1 and 1 to obtain I2, and the calculation function is as follows: in, is the original data, and are the maximum and minimum values ​​in the data.

[0011] Preferably, the specific processing process of S2 is as follows: S21: Take the normalized transformer bushing leakage current I2 as the original signal and select the appropriate wavelet basis function and the number of decomposition levels Then wavelet decomposition is used to decompose the original signal I2 into a series of wavelet coefficients, which are divided into approximate coefficients and detail factor ; S22: Calculation function: in, is the number of decomposition layers, =0, 1, …, ; is the signal length; when =0, That is the original input signal; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter; Is the index of the output coefficient, which is used to determine the filter when calculating the wavelet coefficients and The sampling location.

[0012] S23: Extract all detail coefficients after wavelet decomposition , and use it to construct the detail coefficient matrix .

[0013] Preferably, the specific processing process of S3 is as follows: S31: Establish a KAN filter model, use the B-spline function as a univariate approximator, determine the size of the input layer according to the dimension of the input feature vector, and determine the appropriate hidden layer and output layer structure; The detail coefficient matrix to be established Take it as the input feature vector of the KAN filtering model and filter it; The calculation function is as follows: in, is the input vector; is a matrix operator. The meaning of this operator is to add all the elements in each row of the original matrix and use them as the row elements in the new matrix to form a new vector as the output; Indicates connection Layer and The activation function of the layer; S32: In the process of network training of the KAN filter model, in order to reduce the difference between the output and the expected output to obtain a better filtering effect, the mean square error (MSE) loss function is used to evaluate the model training, and the Adam optimizer is used to update the parameters of the KAN filter model to minimize the loss function.

[0014] The expression of the MSE loss function is as follows: in, is the expected output, For network output, is the sample size; The expression of the Adam optimizer's function for updating model parameters is as follows: in, , are the model parameters before and after iteration respectively; is the learning rate, which is used to control the update step size; and are the corrected first-order moment estimates and second-order moment estimates, respectively; Used to prevent the denominator from being 0; S33: Detail coefficient matrix Input into the trained KAN filter model to obtain the detail coefficient matrix after filtering out the induced current . Using the matrix The detail coefficient after removing the induced current and approximate coefficients Reconstructing the transformer bushing leakage current value ; The expression of the reconstruction function is as follows: in, is the total number of decomposition layers; For the Approximation coefficients of layers; For the The detail factor of the layer, ; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter.

[0015] Preferably, the specific processing process of S4 is as follows: S41: During actual use of the transformer, the transformer sleeve capacitance value will change with the operating environment temperature, humidity and its own aging degree; Construct an empirical model of the transformer bushing capacitance during normal operation and substitute the relevant transformer bushing parameters during operation to more accurately calculate the transformer bushing capacitance. ; The calculation function is as follows: in, is the initial capacitance value of the transformer bushing; is the empirical temperature coefficient of the transformer bushing, , They are respectively the ambient temperature of the transformer bushing when the transformer is operating normally and the reference temperature of the transformer bushing; is the empirical moisture coefficient of the transformer bushing, , They are respectively the ambient humidity of the transformer bushing when the transformer is operating normally and the reference humidity of the transformer bushing; is the empirical aging coefficient of the transformer bushing, , They are respectively the aging degree of the transformer bushing when the transformer is in normal operation and the reference aging degree of the transformer bushing; S42: Calculate the dielectric loss factor of transformer bushings ; The function derivation process is as follows: In the formula, is the capacitor current, is the active current, is the voltage value on both sides of the transformer bushing, is the voltage frequency across the transformer bushing, is the transformer bushing capacitance; S43: Utilization , Calculated transformer bushing capacitance , Transformer bushing dielectric loss factor , calculate the insulation resistance of the transformer bushing ; The calculation function is as follows: In the formula, is the angular frequency of the voltage across the transformer bushing; is the transformer bushing capacitance; is the dielectric loss factor of the transformer bushing.

[0016] (III) Beneficial effects The present invention provides a method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm. It has the following beneficial effects: This method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm takes into account the influence of ambient temperature, humidity and the degree of aging of the transformer bushing when constructing the empirical model of the transformer bushing capacitance. By filtering out the induced current part in the measured value of the transformer bushing leakage current, this method can more accurately calculate the insulation resistance of the transformer bushing, thereby more truly reflecting the insulation performance of the transformer, and generating the detail coefficient matrix through wavelet transform. After passing through the Kans filtering model, the output matrix reconstructs the signal, and then uses the strong function approximation ability and high accuracy of KANs to learn the mapping relationship from the detail coefficient to the detail coefficient after filtering out the induced current, and uses the mean square error loss function and Adam optimizer to train the KAN filtering model, minimize the loss function, improve the filtering effect, further reduce the difference between the output and the expected output, and optimize the performance of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of wavelet transform signal decomposition.

[0018] Figure 2 It is a calculation flow chart of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Step 1: Collect the voltage values ​​at both ends of the transformer bushing and the transformer bushing leakage current value, and normalize the measured value of the transformer bushing leakage current.

[0021] Step 2: Select appropriate wavelet basis functions and decomposition layers, decompose the normalized transformer bushing leakage current value as the original signal, extract the detail coefficients of each layer in the wavelet coefficients, and construct a detail coefficient matrix as the filter input vector.

[0022] Step 3: Establish a KAN filter model, use the trained KAN filter model to process the input detail coefficient matrix, output the detail coefficient matrix after filtering out the induced current, reconstruct the signal to obtain the transformer bushing leakage current after filtering out the induced current.

[0023] Step 4: Calculate the capacitance of the transformer bushing, the dielectric loss factor of the transformer bushing, and finally calculate the insulation resistance of the transformer bushing.

[0024] The specific derivation process in step 1 is as follows: The voltage U1 across the transformer bushing and the transformer bushing leakage current I1 are measured using a voltage transformer and a current transformer. Through MAX-MIN normalization, the measured value I1 of the transformer bushing leakage current is normalized to between -1 and 1 to obtain I2. The calculation function is as shown in formula (1): (1) in, is the original data, and are the maximum and minimum values ​​in the data.

[0025] In step 2, the specific process is: The normalized transformer bushing leakage current I2 is used as the original signal and the appropriate wavelet basis function is selected. and the number of decomposition levels Then wavelet decomposition is used to decompose the original signal I2 into a series of wavelet coefficients, which are divided into approximate coefficients and detail factor The calculation function is as follows: (2) (3) in, is the number of decomposition layers, =0, 1, …, ; is the signal length; when =0, That is the original input signal; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter; Is the index of the output coefficient, which is used to determine the filter when calculating the wavelet coefficients and The sampling location.

[0026] Extract all detail coefficients after wavelet decomposition , and use it to construct the detail coefficient matrix .

[0027] In step 3, the specific process is: The KAN filter model is established, and the B-spline function is used as a univariate approximator. The size of the input layer is determined according to the dimension of the input feature vector, and the appropriate hidden layer and output layer structure are determined. As the input feature vector of the KAN filter model and filter it. The calculation function is as follows: (4) in, is the input vector; is a matrix operator. The meaning of this operator is to add all the elements in each row of the original matrix and use them as the row elements in the new matrix to form a new vector as the output; Indicates connection Layer and The activation function of the layer.

[0028] In the process of network training of the KAN filter model, in order to reduce the difference between the output and the expected output to obtain a better filtering effect, the mean squared error (MSE) loss function is used to evaluate the model training, and the Adam optimizer is used to update the parameters of the KAN filter model to minimize the loss function.

[0029] The expression of the MSE loss function is as follows: (5) in, is the expected output, For network output, is the sample size.

[0030] The expression of the Adam optimizer's function for updating model parameters is as follows: (6) in, , are the model parameters before and after iteration respectively; is the learning rate, which is used to control the update step size; and They are the corrected first-order moment estimate and second-order moment estimate respectively; A very small number (usually 10-8) used to prevent the denominator from being zero.

[0031] The detail coefficient matrix Input into the trained KAN filter model to obtain the detail coefficient matrix after filtering out the induced current . Using the matrix The detail coefficient after removing the induced current and approximate coefficients Reconstructing the transformer bushing leakage current value The expression of the reconstruction function is as follows: (7) in, is the total number of decomposition layers; For the Approximation coefficients of layers; For the The detail factor of the layer, ; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter.

[0032] In step 4, the specific process is: In actual use of transformers, the transformer bushing capacitance value will change with the operating environment temperature, humidity and its own aging degree. Construct an empirical model of the transformer bushing capacitance in normal operation, substitute the relevant transformer bushing parameters in the operation process, and calculate the transformer bushing capacitance more accurately. The calculation function is as follows: (8) in, is the initial capacitance value of the transformer bushing; is the empirical temperature coefficient of the transformer bushing, , They are respectively the ambient temperature of the transformer bushing when the transformer is operating normally and the reference temperature of the transformer bushing; is the empirical moisture coefficient of the transformer bushing, , They are respectively the ambient humidity of the transformer bushing when the transformer is operating normally and the reference humidity of the transformer bushing; is the empirical aging coefficient of the transformer bushing, , They are respectively the aging degree of the transformer bushing during normal operation of the transformer and the reference aging degree of the transformer bushing.

[0033] Calculating the dielectric loss factor of transformer bushings The function derivation process is as follows: (9) (10) (11) In the formula, is the capacitor current, is the active current, is the voltage value on both sides of the transformer bushing, is the voltage frequency across the transformer bushing, is the transformer bushing capacitance.

[0034] The transformer bushing capacitance calculated using equations (8) and (11) is , Transformer bushing dielectric loss factor , calculate the insulation resistance of the transformer bushing The calculation function is as follows: (12) In the formula, is the angular frequency of the voltage across the transformer bushing; is the transformer bushing capacitance; is the dielectric loss factor of the transformer bushing.

[0035] Assume that the problem to be solved is a substation with a voltage level of more than 500KV. In the process of calculating the insulation resistance of the transformer bushing by measuring the leakage current of the transformer bushing to reflect the insulation performance of the transformer bushing and the transformer, due to electromagnetic interference, the measured value of the transformer bushing leakage current contains an induced current part. The calculated insulation resistance of the transformer bushing is not accurate and cannot accurately reflect the insulation performance of the transformer bushing and the transformer.

[0036] Then, firstly, by selecting voltage transformer and current transformer of appropriate range, and connecting them correctly, the voltage value U1 at both ends of the transformer bushing and the measured value I1 of the transformer bushing leakage current are measured, and the measured value I1 of the transformer bushing leakage current is normalized to I2.

[0037] The measured value I2 of the transformer bushing leakage current is taken as the original signal, and the original signal is decomposed into a series of wavelet signals through the wavelet transform algorithm, and the total detail coefficient Combined construction of detail coefficient matrix As the input vector of the KAN filter model, the trained KAN filter model is used to filter out the induced electric components in the wavelet signal and output the detail coefficient matrix after removing the induced current. , reconstruct the signal to obtain a more accurate transformer bushing leakage current value .

[0038] Construct an empirical calculation model for transformer bushing capacitance to calculate the capacitance of transformer bushing , Dielectric loss factor of transformer bushing Substitute the above calculated parameters into the capacitive equipment insulation resistance calculation formula to obtain the transformer bushing insulation resistance .

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm, characterized in that: The steps include: S1: Collect the voltage value at both ends of the transformer bushing and the transformer bushing leakage current value, and normalize the measured value of the transformer bushing leakage current; S2: Select appropriate wavelet basis functions and decomposition layers, decompose the normalized transformer bushing leakage current value as the original signal, extract the detail coefficients of each layer in the wavelet coefficients to construct a detail coefficient matrix as the filter input vector; S3: Establish a KAN filter model, use the trained KAN filter model to process the input detail coefficient matrix, output the detail coefficient matrix after filtering out the induced current, reconstruct the signal to obtain the transformer bushing leakage current after filtering out the induced current; S4: Calculate the capacitance of the transformer bushing, the dielectric loss factor of the transformer bushing, and finally calculate the insulation resistance of the transformer bushing.

2. The method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm according to claim 1 is characterized in that: The specific processing process of S1 is as follows: the voltage U1 at both ends of the transformer bushing and the transformer bushing leakage current I1 are measured by using a voltage transformer and a current transformer. Through MAX-MIN normalization, the measured value I1 of the transformer bushing leakage current is normalized to between -1 and 1 to obtain I2, and the calculation function is as follows: in, is the original data, and are the maximum and minimum values ​​in the data.

3. The method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm according to claim 1 is characterized in that: The specific processing process of S2 is as follows: S21: Take the normalized transformer bushing leakage current I2 as the original signal and select the appropriate wavelet basis function and the number of decomposition levels Then wavelet decomposition is used to decompose the original signal I2 into a series of wavelet coefficients, which are divided into approximate coefficients and detail factor ; S22: Calculation function: in, is the number of decomposition layers, =0, 1, …, ; is the signal length; when =0, That is the original input signal; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter; Is the index of the output coefficient, which is used to determine the filter when calculating the wavelet coefficients and The sampling location. S23: Extract all detail coefficients after wavelet decomposition , and use it to construct the detail coefficient matrix .

4. The method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm according to claim 1 is characterized in that: The specific processing process of S3 is as follows: S31: Establish a KAN filter model, use the B-spline function as a univariate approximator, determine the size of the input layer according to the dimension of the input feature vector, and determine the appropriate hidden layer and output layer structure; The detail coefficient matrix to be established Take it as the input feature vector of the KAN filtering model and filter it; The calculation function is as follows: in, is the input vector; is a matrix operator. The meaning of this operator is to add all the elements in each row of the original matrix and use them as the row elements in the new matrix to form a new vector as the output; Indicates connection Layer and The activation function of the layer; S32: In the process of network training of the KAN filter model, in order to reduce the difference between the output and the expected output to obtain a better filtering effect, the mean square error (MSE) loss function is used to evaluate the model training, and the Adam optimizer is used to update the parameters of the KAN filter model to minimize the loss function. The expression of the MSE loss function is as follows: in, is the expected output, For network output, is the sample size; The expression of the Adam optimizer's function for updating model parameters is as follows: in, , are the model parameters before and after iteration respectively; is the learning rate, which is used to control the update step size; and They are the corrected first-order moment estimate and second-order moment estimate respectively; Used to prevent the denominator from being 0; S33: Detail coefficient matrix Input into the trained KAN filter model to obtain the detail coefficient matrix after filtering out the induced current . Using the matrix The detail coefficient after removing the induced current and approximate coefficients Reconstructing the transformer bushing leakage current value ; The expression of the reconstructed function is as follows: in, is the total number of decomposition layers; For the Approximation coefficients of layers; For the The detail factor of the layer, ; It is a low-pass filter, also called a scaling function filter; It is a high-pass filter, also called a wavelet function filter.

5. The method for calculating the insulation resistance of transformer bushing under electromagnetic interference based on the Wav-KAN algorithm according to claim 1 is characterized in that: The specific processing process of S4 is as follows: S41: During actual use of the transformer, the transformer sleeve capacitance value will change with the operating environment temperature, humidity and its own aging degree; Construct an empirical model of the transformer bushing capacitance during normal operation and substitute the relevant transformer bushing parameters during operation to more accurately calculate the transformer bushing capacitance. ; The calculation function is as follows: in, is the initial capacitance value of the transformer bushing; is the empirical temperature coefficient of the transformer bushing, , They are respectively the ambient temperature of the transformer bushing when the transformer is operating normally and the reference temperature of the transformer bushing; is the empirical moisture coefficient of the transformer bushing, , They are respectively the ambient humidity of the transformer bushing when the transformer is operating normally and the reference humidity of the transformer bushing; is the empirical aging coefficient of the transformer bushing, , They are respectively the aging degree of the transformer bushing when the transformer is in normal operation and the reference aging degree of the transformer bushing; S42: Calculate the dielectric loss factor of transformer bushings ; The function derivation process is as follows: In the formula, is the capacitor current, is the active current, is the voltage value on both sides of the transformer bushing, is the voltage frequency across the transformer bushing, is the transformer bushing capacitance; S43: Utilization , Calculated transformer bushing capacitance , Transformer bushing dielectric loss factor , calculate the insulation resistance of the transformer bushing ; The calculation function is as follows: In the formula, is the angular frequency of the voltage across the transformer bushing; is the transformer bushing capacitance; is the dielectric loss factor of the transformer bushing.

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